code: def kernel_cdf_recipinvgauss(x, sample, bw):
      # Reciprocal inverse gaussian kernel for cdf estimation
  
      # need shape-scale parameterization for scipy
      # references use m, lambda parameterization
      m = 1 / (x - bw)
      lam = 1 / bw
      return stats.recipinvgauss.sf(sample, m / lam, scale=1 / lam)
  
imports: import numpy
  from scipy import special, stats
label_desc: Use image node_id 20 for calling a global function with example usage: kernel_cdf_recipinvgauss(x, sample, bw) and returns: stats